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http://dx.doi.org/10.13088/jiis.2018.24.1.141

Mining Intellectual History Using Unstructured Data Analytics to Classify Thoughts for Digital Humanities  

Seo, Hansol (School of Management Kyung Hee University)
Kwon, Ohbyung (School of Management Kyung Hee University)
Publication Information
Journal of Intelligence and Information Systems / v.24, no.1, 2018 , pp. 141-166 More about this Journal
Abstract
Information technology improves the efficiency of humanities research. In humanities research, information technology can be used to analyze a given topic or document automatically, facilitate connections to other ideas, and increase our understanding of intellectual history. We suggest a method to identify and automatically analyze the relationships between arguments contained in unstructured data collected from humanities writings such as books, papers, and articles. Our method, which is called history mining, reveals influential relationships between arguments and the philosophers who present them. We utilize several classification algorithms, including a deep learning method. To verify the performance of the methodology proposed in this paper, empiricists and rationalism - related philosophers were collected from among the philosophical specimens and collected related writings or articles accessible on the internet. The performance of the classification algorithm was measured by Recall, Precision, F-Score and Elapsed Time. DNN, Random Forest, and Ensemble showed better performance than other algorithms. Using the selected classification algorithm, we classified rationalism or empiricism into the writings of specific philosophers, and generated the history map considering the philosopher's year of activity.
Keywords
Digital Humanities; History Mining; Text Analysis; Philosophy; Classification Algorithms;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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